Story analysis of players

rm(list = ls())
library("rstudioapi")
setwd(dirname(getActiveDocumentContext()$path))

Uncomment and run this block if you haven’t had these packages installed

# install.packages("ona", repos = c("https://cran.qe-libs.org", "https://cran.rstudio.org"))
# install.packages("tma", repos = c("https://cran.qe-libs.org", "https://cran.rstudio.org"))
# install.packages("magrittr")

0. setting up constant variables (change as appropriate)

mapid = "23230129"
# mapid = "24096504"
wd = paste0(getwd(), "/data/")

0. read A B C matrices and other relevant data

A_matrix = read_csv(paste0(wd, mapid, "_a_matrix.csv"))
B_matrix = read_csv(paste0(wd, mapid, "_b_matrix.csv"))
C_matrix = read.csv(paste0(wd, mapid, "_c_matrix.csv"))
sh_info = read.csv(paste0(wd, "LEM text - Stakeholders.csv"))
A = jsonlite::read_json(paste0(wd, mapid, "_init_map.json"))
reps = jsonlite::read_json(paste0(wd, mapid, "_reps.json"))
sub_result = readRDS(paste0(wd, mapid, "a_sub_result_new.rds")) #record satisfy level

sh_name = A_matrix %>%
  select(c(Name)) %>%
  unique() %>%
  pull()

sh_info = sh_info %>% 
  filter(Name %in% sh_name) %>% 
  arrange(match(Name, sh_name)) %>% 
  unite("SHinfo", Name:Direction, remove = TRUE) %>% 
  pull()

name_col = sh_name
ind_col = c()
dir_col = c()
group_col = c()
for (i in 1:length(A$indicators)) {
  for (j in 1:length(A$indicators[[i]]$stakeholders)) {
    ind_col <- append(ind_col, A$indicators[[i]]$stakeholders[[j]]$indKey)
    dir_col <- append(dir_col, A$indicators[[i]]$stakeholders[[j]]$direction)
    group_col <- append(group_col, A$indicators[[i]]$stakeholders[[j]]$stakeholderGroup)
  }
}
sh_df <- data.frame(name = name_col, indicator = ind_col, direction = dir_col, group = group_col)
sh_df

business <- sh_df %>% filter(group == "Local Business Consortium")
business <- business$name
environmental <- sh_df %>% filter(group == "Environmental Justice Center")
environmental <- environmental$name
community <- sh_df %>% filter(group == "Community Coalition")
community <- community$name

submission_name = C_matrix$user_name
submission_order = c()
sub_approval_count = c()
sub_order_num= c()


for (i in 1:length(sub_result)){
  submission_order = append(submission_order, sub_result[[i]]$userKey)
  sub_order_num = append(sub_order_num, sub_result[[i]]$submissionCount)
  sub_approval_count = append(sub_approval_count, sub_result[[i]]$approvalCount)
}

submission_df <- data.frame(userKey = submission_order, submissionCount = sub_order_num, approvalCount = sub_approval_count)
uniqueUsers <- unique(submission_df$userKey)
print(submission_df)

print(uniqueUsers)

 [1] "Benji"           "isahugh03"       "Kukuw27"         "hayleysalthouse" "cmangum"         "trentonjackson1" "sethapple123"   
 [8] "christianatw4"   "catw4"           "jkuethe03"       "matthewb08675"   "urusai"          "LED_57"          "Mercedes"       
[15] "abrooks11"       "ghinamoh07"      "Mark.Blakely"    "AidanC126"       "Richard"         "mckenzieakbari"  "kendalljackson" 
[22] "planetpat12"     "MichaelGaines"   "igudino"         "trentonjackson"  "Lexiiiii"        "carolinemott"    "nghiavo"        
[29] "SOS001F"         "Jimmy"           "LeoMunoz"        "WilliamJPayne"   "brookea1255"     "tigisesay"       "ChloeLineberry" 
[36] "albertaeinstein" "melissak1"       "benswit123"      "cwhitmore"       "jforbes"         "amandaPdavis"    "lilyking1103"   
[43] "landenramsey"    "Mshah1"          "emmamcnamara"    "katierakes27"    "twhitf496"       "amrit204"        "landyn.roberts" 
[50] "CalebHeacox"     "Rubyd1"         

Filter only users with more than 2 submissions

uniqueUsers_filtered <- submission_df %>%
  group_by(userKey) %>% 
  summarise(Count = n()) %>%
  filter(Count > 2)

uniqueUsers_filtered <- uniqueUsers_filtered$userKey

numPlots <- 7 # adjust as appropriate
batchSize <- ceiling(length(uniqueUsers_filtered) / numPlots)

for (i in 1:numPlots) {
  batch <- submission_df %>% filter(userKey %in% uniqueUsers_filtered[(((i-1) * batchSize) + 1):(min(length(uniqueUsers_filtered), i * batchSize))])
  
  # Create a line plot using ggplot2
  p <- ggplot(batch, aes(x = submissionCount, y = approvalCount, color = userKey, linetype = userKey)) +
    geom_line(size=1) +
    scale_x_continuous(breaks = 1:nrow(submission_df), labels = 1:nrow(submission_df), minor_breaks = NULL) +
    scale_y_continuous(breaks = seq(0, max(submission_df$approvalCount), by = 1), minor_breaks = NULL) +
    theme_minimal()
  print(p)
}

Prepare dataframe for stacked barplot

submission_detailed_df <- data.frame(userKey = character(), submissionCount = integer(), business = integer(),
                                     businessMax = integer(), environmental = integer(), environmentalMax = integer(),
                                     community = integer(), communityMax = integer())
for (i in 1:length(sub_result)) {
  if (sub_result[[i]]$userKey %in% uniqueUsers_filtered) {
    b_max = sum(business %in% sub_result[[i]]$stakeholders)
    e_max = sum(environmental %in% sub_result[[i]]$stakeholders)
    c_max = sum(community %in% sub_result[[i]]$stakeholders)
    b_select = sub_result[[i]]$stakeholders[sub_result[[i]]$stakeholders %in% business]
    e_select = sub_result[[i]]$stakeholders[sub_result[[i]]$stakeholders %in% environmental]
    c_select = sub_result[[i]]$stakeholders[sub_result[[i]]$stakeholders %in% community]
    b_count = 0
    e_count = 0
    c_count = 0
    for (b in b_select) {
      b_count = b_count + sub_result[[i]]$stakeholderApproval[[b]]
    }
    for (e in e_select) {
      e_count = e_count + sub_result[[i]]$stakeholderApproval[[e]]
    }
    for (c in c_select) {
      c_count = c_count + sub_result[[i]]$stakeholderApproval[[c]]
    }
    submission_detailed_df <- rbind(submission_detailed_df, data.frame(userKey = sub_result[[i]]$userKey,
                                    submissionCount = sub_result[[i]]$submissionCount, business = b_count,
                                    businessMax = b_max, environmental = e_count, environmentalMax = e_max,
                                    community = c_count, communityMax = c_max))
  }
}

bar_width = 0.5
dodge_width <- 0  # Adjust this as needed
expand_mults <- c(0, 0.5)
submissionCount_levels <- as.character(unique(submission_detailed_df$submissionCount))
for (user in uniqueUsers_filtered) {
  b_plot <- ggplot(submission_detailed_df %>% filter(userKey == user), aes(x = factor(submissionCount))) +
    geom_bar(aes(y = businessMax), stat = "identity", fill = "gainsboro", position = position_dodge(width = dodge_width), width = bar_width) +
    geom_bar(aes(y = business), stat = "identity", fill = "cornflowerblue", position = position_dodge(width = dodge_width), width = bar_width) +
    labs(title = "Business",
         x = "submissionCount",
         y = "count",
         fill = "Variable") +
    theme_minimal()
  # +
  #   scale_x_discrete(expand = expand_scale(mult = expand_mults, add = c(0, 0)))
  # Assuming the range of your y-axis is known or can be calculated:
  y_max <- max(submission_detailed_df$businessMax, na.rm = TRUE)
  # Create a sequence from 1 to the maximum value of y, by 1 unit interval
  hlines <- seq(1, y_max, by = 1)
  # Add horizontal lines to the plot
  b_plot <- b_plot + geom_hline(yintercept = hlines, color = "white", linetype = "solid")
  
  e_plot <- ggplot(submission_detailed_df %>% filter(userKey == user), aes(x = factor(submissionCount))) +
    geom_bar(aes(y = environmentalMax), stat = "identity", fill = "gainsboro", position = position_dodge(width = dodge_width), width = bar_width) +
    geom_bar(aes(y = environmental), stat = "identity", fill = "forestgreen", position = position_dodge(width = dodge_width), width = bar_width) +
    labs(title = "Environmental",
         x = "submissionCount",
         y = "count",
         fill = "Variable") +
    theme_minimal()
  y_max <- max(submission_detailed_df$environmentalMax, na.rm = TRUE)
  hlines <- seq(1, y_max, by = 1)
  e_plot <- e_plot + geom_hline(yintercept = hlines, color = "white", linetype = "solid")
  
  c_plot <- ggplot(submission_detailed_df %>% filter(userKey == user), aes(x = factor(submissionCount))) +
    geom_bar(aes(y = communityMax), stat = "identity", fill = "gainsboro", position = position_dodge(width = dodge_width), width = bar_width) +
    geom_bar(aes(y = community), stat = "identity", fill = "darkorange", position = position_dodge(width = dodge_width), width = bar_width) +
    labs(title = "Community",
         x = "submissionCount",
         y = "count",
         fill = "Variable") +
    theme_minimal()
  y_max <- max(submission_detailed_df$communityMax, na.rm = TRUE)
  hlines <- seq(1, y_max, by = 1)
  c_plot <- c_plot + geom_hline(yintercept = hlines, color = "white", linetype = "solid")
  
  print((b_plot / e_plot / c_plot) +
    plot_annotation(title = user))
}

ONA Section

1. combine conservation and limited use and set self-connection to zero

which means luc_1_X + luc_4_X, luc_X_1 + luc_X_4

A_matrix_2 <- A_matrix
B_matrix_2 <- B_matrix
C_matrix_2 <- C_matrix
for (i in 1:11) {
  A_luc1x = which(names(A_matrix_2)==paste0("luc_", 1, "_", i-1))
  A_luc4x = which(names(A_matrix_2)==paste0("luc_", 4, "_", i-1))
  B_luc1x = which(names(B_matrix_2)==paste0("luc_", 1, "_", i-1))
  B_luc4x = which(names(B_matrix_2)==paste0("luc_", 4, "_", i-1))
  C_luc1x = which(names(C_matrix_2)==paste0("luc_", 1, "_", i-1))
  C_luc4x = which(names(C_matrix_2)==paste0("luc_", 4, "_", i-1))
  A_matrix_2[[A_luc1x]] <- A_matrix_2[[A_luc1x]] + A_matrix_2[[A_luc4x]]
  B_matrix_2[[B_luc1x]] <- B_matrix_2[[B_luc1x]] + B_matrix_2[[B_luc4x]]
  C_matrix_2[[C_luc1x]] <- C_matrix_2[[C_luc1x]] + C_matrix_2[[C_luc4x]]
}
for (i in 1:11) {
  A_lucx1 = which(names(A_matrix_2)==paste0("luc_", i-1, "_", 1))
  A_lucx4 = which(names(A_matrix_2)==paste0("luc_", i-1, "_", 4))
  B_lucx1 = which(names(B_matrix_2)==paste0("luc_", i-1, "_", 1))
  B_lucx4 = which(names(B_matrix_2)==paste0("luc_", i-1, "_", 4))
  C_lucx1 = which(names(C_matrix_2)==paste0("luc_", i-1, "_", 1))
  C_lucx4 = which(names(C_matrix_2)==paste0("luc_", i-1, "_", 4))
  A_matrix_2[[A_lucx1]] <- A_matrix_2[[A_lucx1]] + A_matrix_2[[A_lucx4]]
  B_matrix_2[[B_lucx1]] <- B_matrix_2[[B_lucx1]] + B_matrix_2[[B_lucx4]]
  C_matrix_2[[C_lucx1]] <- C_matrix_2[[C_lucx1]] + C_matrix_2[[C_lucx4]]
}
for (i in 1:11) {
  A_lucxx = which(names(A_matrix_2)==paste0("luc_", i-1, "_", i-1))
  B_lucxx = which(names(B_matrix_2)==paste0("luc_", i-1, "_", i-1))
  C_lucxx = which(names(C_matrix_2)==paste0("luc_", i-1, "_", i-1))
  A_matrix_2[[A_lucxx]] = 0
  B_matrix_2[[B_lucxx]] = 0
  C_matrix_2[[C_lucxx]] = 0
}

A_matrix_2 <- A_matrix_2 %>% select(-c(luc_4_0, luc_4_1, luc_4_2, luc_4_3, luc_4_4, 
                               luc_4_5, luc_4_6, luc_4_7, luc_4_8, luc_4_9, luc_4_10,
                               luc_0_4, luc_1_4, luc_2_4, luc_3_4, luc_4_4,
                               luc_5_4, luc_6_4, luc_7_4, luc_8_4, luc_9_4, luc_10_4))
B_matrix_2 <- B_matrix_2 %>% select(-c(luc_4_0, luc_4_1, luc_4_2, luc_4_3, luc_4_4, 
                               luc_4_5, luc_4_6, luc_4_7, luc_4_8, luc_4_9, luc_4_10,
                               luc_0_4, luc_1_4, luc_2_4, luc_3_4, luc_4_4,
                               luc_5_4, luc_6_4, luc_7_4, luc_8_4, luc_9_4, luc_10_4))
C_matrix_2 <- C_matrix_2 %>% select(-c(luc_4_0, luc_4_1, luc_4_2, luc_4_3, luc_4_4, 
                               luc_4_5, luc_4_6, luc_4_7, luc_4_8, luc_4_9, luc_4_10,
                               luc_0_4, luc_1_4, luc_2_4, luc_3_4, luc_4_4,
                               luc_5_4, luc_6_4, luc_7_4, luc_8_4, luc_9_4, luc_10_4))

2. make ABC col names consistent for the ease of later work

colnames(A_matrix_2)[2] = "SH"
A_matrix_2 <- A_matrix_2 %>%
  select(-c(...1)) %>% 
  add_column(user_name = "NA", .before = "luc_0_0") %>%
  add_column(submission = "NA",.before = "luc_0_0") %>%
  add_column(index = 1:nrow(A_matrix), .before = "SH")

colnames(B_matrix_2)[2] = "SH"
B_matrix_2 <- B_matrix_2 %>%
  select(-c(...1)) %>% 
  add_column(user_name = "NA", .before = "luc_0_0") %>%
  add_column(submission = "NA",.before = "luc_0_0") %>%
  add_column(index = 1:nrow(B_matrix), .before = "SH")

C_matrix_2 <- C_matrix_2 %>%
  add_column(index = 1:nrow(C_matrix_2), .before = "user_name") %>%
  add_column(Name = "NA", .before = "user_name") %>%
  add_column(Satisfy = "NA",.before = "user_name") %>%
  add_column(N = "NA", .before = "user_name")

3.run ONA

call generate.ona.object function

generate.ona.object <- function(data, unit.col, meta.col, codes){
  output <- list()
  f.units <- unit.col
  ENA_UNIT <- rENA::merge_columns_c(f.units, cols = colnames(f.units));
  # ENA_UNIT <-  f.units
  f.raw <- data
  f.codes <- codes
  dena_data = directedENA:::ena.set.directed(f.raw, f.units, NA, f.codes)
  output.meta.data <- meta.col
  dena_data$meta.data <- data.table::as.data.table(cbind(ENA_UNIT, output.meta.data))
  for( i in colnames(dena_data$meta.data) ) {
    set(dena_data$meta.data, j = i, value = rENA::as.ena.metadata(dena_data$meta.data[[i]]))
  }
  code_length <- length(dena_data$rotation$codes);
  dena_data$rotation$adjacency.key <- data.table::data.table(matrix(c(
    rep(1:code_length, code_length),
    rep(1:code_length, each = code_length)),
    byrow = TRUE, nrow = 2
  ))
  directed.adjacency.vectors <- as.ena.matrix(data.table::as.data.table(data[, grep("V", colnames(data))]), "ena.connections")
  # aren't these two the same thing 
  dena_data$connection.counts <- data.table::as.data.table(cbind(dena_data$meta.data, directed.adjacency.vectors))
  dena_data$connection.counts = rENA::as.ena.matrix(x = dena_data$connection.counts, "ena.connections")
  for (i in which(!rENA::find_meta_cols(dena_data$connection.counts)))
    set(dena_data$connection.counts, j = i, value = as.ena.co.occurrence(as.double(dena_data$connection.counts[[i]])))
  dena_data$model$row.connection.counts = data.table::as.data.table(cbind(dena_data$meta.data, directed.adjacency.vectors))
  dena_data$model$row.connection.counts <- rENA::as.ena.matrix(dena_data$model$row.connection.counts, "row.connections")
  output = dena_data;
  return(output)
}

# 1. prepare df

# df <- A_matrix
# df <- B_matrix
df <- C_matrix_2

names(df)[7:106] <- paste0("V",seq(1:100))
df$mapid = mapid

# 2. accum
accumC <- generate.ona.object(
  df,
  unit.col = df[,1:7],
  meta.col = df[,1:7],
  codes = as.vector(
    c("Commercial",
    "Conservation_lu",
    # "Conservation",
    "Cropland",
    "Industrial",
    # "Limited Use",
    "Pasture",
    "Recreation",
    "Residential HD",
    "Residential LD",
    "Timber",
    "Wetlands")))

# 3. model
setC <- model(accumC)

warning: solve(): system is singular; attempting approx solution

warning: solve(): system is singular; attempting approx solution

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# 4. GoF
correlations(setC)

NA

# 1. prepare df

df1 <- A_matrix_2
# df <- B_matrix
# df <- C_matrix

names(df1)[7:106] <- paste0("V",seq(1:100))
df1$mapid = mapid

# 2. accum
accumA <- generate.ona.object(
  df1,
  unit.col = df1[,1:7],
  meta.col = df1[,1:7],
  codes = as.vector(
    c("Commercial",
    "Conservation_lu",
    # "Conservation",
    "Cropland",
    "Industrial",
    # "Limited Use",
    "Pasture",
    "Recreation",
    "Residential HD",
    "Residential LD",
    "Timber",
    "Wetlands")))

# 3. model
setA <- model(accumA)

# 4. GoF
correlations(setA)

4. project C points into A space

To do so, I need to make a set using C’s accumulation and A’s rotation matrix

set=model(accumC, rotation.set = setA$rotation)

5. ona plotting

5.1 setting global visual parameters here so that all ONA plots we generate are on the same scale

node_size_multiplier = 0.2
node_position_multiplier = 1.0
edge_size_multiplier = 0.2
point_position_multiplier = 1.0
edge_arrow_saturation_multiplier = 1.0

ona:::plot.ena.ordered.set(setC) %>% 
  units( 
    points = setC$points,
    points_color = "black",
    show_mean = FALSE, show_points = TRUE, with_ci = FALSE)

5.2 quick check points ARE being rotated

ona:::plot.ena.ordered.set(setA, title = "space used for projection and its points") %>%
  units(
    points = setA$points,
    points_color = "gray",
    show_mean = TRUE, show_points = TRUE, with_ci = FALSE) %>%
 nodes(
    node_size_multiplier = 0.001) 

ona:::plot.ena.ordered.set(setC, title = "points need to be projected in its original space") %>%
  units( 
    points = setC$points,
    points_color = "black",
    show_mean = FALSE, show_points = TRUE, with_ci = FALSE)%>%
 nodes(
    node_size_multiplier = 0.001) 

ona:::plot.ena.ordered.set(set, title = "projected points in its new space") %>%
  units(
    points = set$points,
    points_color = "black",
    show_mean = FALSE, show_points = TRUE, with_ci = FALSE) %>%
  units(
    points = setA$points,
    points_color = "gray",
    show_mean = FALSE, show_points = TRUE, with_ci = FALSE) %>%
 nodes(
    node_size_multiplier = 0.001) 

Cluster Check:

ona:::plot.ena.ordered.set(setA, title = "space used for projection and its points") %>%
  units(
    points = setA$points,
    points_color = "gray",
    show_mean = TRUE, show_points = TRUE, with_ci = FALSE) %>%
 nodes(
    node_size_multiplier = 0.001) 

A = setA$points[ENA_DIRECTION== 'response']

Plot both bar plots and ona plots by specifying user

uniqueUsers_filtered

 [1] "AidanC126"       "ChloeLineberry"  "Jimmy"           "Kukuw27"         "LED_57"          "LeoMunoz"        "Lexiiiii"       
 [8] "Mark.Blakely"    "MichaelGaines"   "Mshah1"          "Richard"         "SOS001F"         "abrooks11"       "albertaeinstein"
[15] "amrit204"        "benswit123"      "brookea1255"     "carolinemott"    "catw4"           "christianatw4"   "cmangum"        
[22] "cwhitmore"       "emmamcnamara"    "ghinamoh07"      "hayleysalthouse" "igudino"         "isahugh03"       "jforbes"        
[29] "jkuethe03"       "katierakes27"    "kendalljackson"  "landenramsey"    "landyn.roberts"  "lilyking1103"    "matthewb08675"  
[36] "mckenzieakbari"  "melissak1"       "nghiavo"         "planetpat12"     "sethapple123"    "tigisesay"       "trentonjackson" 
[43] "trentonjackson1" "twhitf496"       "urusai"         

userkey = "AidanC126"

subs <- as.list(unique(set$points$ENA_UNIT))
subs <- subs[grepl(userkey, subs)] 
ona_plots <- list()

for (i in subs) {
  p <- ona:::plot.ena.ordered.set(set, title = paste0(i)) %>%
  edges(
      weights = set$line.weights[ENA_UNIT == i],
      edge_size_multiplier = edge_size_multiplier,
      edge_arrow_saturation_multiplier = edge_arrow_saturation_multiplier,
      node_position_multiplier = node_position_multiplier,
      edge_color = c("red")) %>%
  nodes(
    node_size_multiplier = node_size_multiplier,
    node_position_multiplier = node_position_multiplier,
    self_connection_color = "red")  %>%
  units(
      points=set$points[ENA_UNIT == i],
      points_color = "black",
      point_position_multiplier = point_position_multiplier,
      show_mean = FALSE, show_points = TRUE, with_ci = FALSE) %>%
  units(
      points = setA$points[SH == sh_name[1] & Satisfy == "Yes"],
      points_color = "green",
      point_position_multiplier = point_position_multiplier,
      show_mean = TRUE, show_points = FALSE, with_ci = FALSE) %>%
  units(
      points = setA$points[SH == sh_name[2] & Satisfy == "Yes"],
      points_color = "blue",
      point_position_multiplier = point_position_multiplier,
      show_mean = TRUE, show_points = FALSE, with_ci = FALSE) %>%
  units(
      points = setA$points[SH == sh_name[3] & Satisfy == "Yes"],
      points_color = "brown",
      point_position_multiplier = point_position_multiplier,
      show_mean = TRUE, show_points = FALSE, with_ci = FALSE) %>%
  units(
      points = setA$points[SH == sh_name[4] & Satisfy == "Yes"],
      points_color = "purple",
      point_position_multiplier = point_position_multiplier,
      show_mean = TRUE, show_points = FALSE, with_ci = FALSE) %>%
  units(
      points = setA$points[SH == sh_name[5] & Satisfy == "Yes"],
      points_color = "yellow",
      point_position_multiplier = point_position_multiplier,
      show_mean = TRUE, show_points = FALSE, with_ci = FALSE) %>%
  units(
      points = setA$points[SH == sh_name[6] & Satisfy == "Yes"],
      points_color = "deeppink",
      point_position_multiplier = point_position_multiplier,
      show_mean = TRUE, show_points = FALSE, with_ci = FALSE) %>%
  units(
      points = setA$points[SH == sh_name[7] & Satisfy == "Yes"],
      points_color = "Tan",
      point_position_multiplier = point_position_multiplier,
      show_mean = TRUE, show_points = FALSE, with_ci = FALSE) %>%
  units(
      points = setA$points[SH == sh_name[8] & Satisfy == "Yes"],
      points_color = "Cyan",
      point_position_multiplier = point_position_multiplier,
      show_mean = TRUE, show_points = FALSE, with_ci = FALSE) %>%
  units(
      points = setA$points[SH == sh_name[9] & Satisfy == "Yes"],
      points_color = "orange",
      point_position_multiplier = point_position_multiplier,
      show_mean = TRUE, show_points = FALSE, with_ci = FALSE)%>% 
      plotly::layout(showlegend = TRUE, legend = list(x = 100, y = 0.9)) %>% 
      style(name = "point", traces = c(2)) %>% 
    style(name = sh_info[1], traces = c(3)) %>% 
    style(name = sh_info[2], traces = c(4)) %>% 
    style(name = sh_info[3], traces = c(5)) %>% 
    style(name = sh_info[4], traces = c(6)) %>% 
    style(name = sh_info[5], traces = c(7)) %>% 
    style(name = sh_info[6], traces = c(8)) %>% 
    style(name = sh_info[7], traces = c(9)) %>% 
    style(name = sh_info[8], traces = c(10)) %>% 
    style(name = sh_info[9], traces = c(11))
  ona_plots[[i]] <- p
}

bar_width = 0.5
dodge_width <- 0  # Adjust this as needed
expand_mults <- c(0, 0.5)
submissionCount_levels <- as.character(unique(submission_detailed_df$submissionCount))

b_plot <- ggplot(submission_detailed_df %>% filter(userKey == userkey), aes(x = factor(submissionCount))) +
  geom_bar(aes(y = businessMax), stat = "identity", fill = "gainsboro", position = position_dodge(width = dodge_width), width = bar_width) +
  geom_bar(aes(y = business), stat = "identity", fill = "cornflowerblue", position = position_dodge(width = dodge_width), width = bar_width) +
  labs(title = "Business",
       x = "submissionCount",
       y = "count",
       fill = "Variable") +
  theme_minimal()
# +
#   scale_x_discrete(expand = expand_scale(mult = expand_mults, add = c(0, 0)))
# Assuming the range of your y-axis is known or can be calculated:
y_max <- max(submission_detailed_df$businessMax, na.rm = TRUE)
# Create a sequence from 1 to the maximum value of y, by 1 unit interval
hlines <- seq(1, y_max, by = 1)
# Add horizontal lines to the plot
b_plot <- b_plot + geom_hline(yintercept = hlines, color = "white", linetype = "solid")

e_plot <- ggplot(submission_detailed_df %>% filter(userKey == userkey), aes(x = factor(submissionCount))) +
  geom_bar(aes(y = environmentalMax), stat = "identity", fill = "gainsboro", position = position_dodge(width = dodge_width), width = bar_width) +
  geom_bar(aes(y = environmental), stat = "identity", fill = "forestgreen", position = position_dodge(width = dodge_width), width = bar_width) +
  labs(title = "Environmental",
       x = "submissionCount",
       y = "count",
       fill = "Variable") +
  theme_minimal()
y_max <- max(submission_detailed_df$environmentalMax, na.rm = TRUE)
hlines <- seq(1, y_max, by = 1)
e_plot <- e_plot + geom_hline(yintercept = hlines, color = "white", linetype = "solid")

c_plot <- ggplot(submission_detailed_df %>% filter(userKey == userkey), aes(x = factor(submissionCount))) +
  geom_bar(aes(y = communityMax), stat = "identity", fill = "gainsboro", position = position_dodge(width = dodge_width), width = bar_width) +
  geom_bar(aes(y = community), stat = "identity", fill = "darkorange", position = position_dodge(width = dodge_width), width = bar_width) +
  labs(title = "Community",
       x = "submissionCount",
       y = "count",
       fill = "Variable") +
  theme_minimal()
y_max <- max(submission_detailed_df$communityMax, na.rm = TRUE)
hlines <- seq(1, y_max, by = 1)
c_plot <- c_plot + geom_hline(yintercept = hlines, color = "white", linetype = "solid")

print((b_plot / e_plot / c_plot) +
  plot_annotation(title = userkey))

for (p in ona_plots) {
  print(p)
}